code: def __init__(self, params):
      self.params = params
  
      self.train_batch_size = params.train_batch_size
      self.val_batch_size = params.val_batch_size
      self.test_batch_size = params.test_batch_size
  
      self.data_dir = params.data_dir
      self.max_seq_length = params.max_seq_length
      self.tokenizer = BertTokenizer(
          vocab_file=os.path.join(params.bert_model_dir, "vocab.txt"),
          do_lower_case=False,
      )
      self.data_cache = params.data_cache
  
file_path: DeepKE/src/deepke/triple_extraction/PRGC/dataloader.py
imports: import os
  import json
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Dataset
  from transformers import BertTokenizer
  from .dataloader_utils import read_examples, convert_examples_to_features
label_desc: Use image node_id 1 to create a new CustomDataLoader object from inherited base classes: object with example: obj = CustomDataLoader(params)